想测测视觉语言模型能不能真在3D空间里干活？SceneActBench给你5个任务、520个案例，开源又公平，看哪个模型分高。
SceneActBench 是一个评估视觉语言模型（VLM）代理在3D场景中行动能力的基准，包含5个任务，覆盖210个源实例和520个任务案例。每个任务在统一的代理-环境循环中运行，使用任务特定的几何指标评估最终输出。在11个专有VLM配置上测试，总体得分范围为38.6到50.2，没有任何配置在所有任务上表现一致。论文分析了失败模式，指出当前模型在多对象3D场景中的行动能力仍有显著不足。
SceneActBench: Can Agents Act on the 3D Scenes They See?
Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operations, leaving agent action on complete multi-object 3D scenes under evaluated. We present SceneActBench, a benchmark for visually conditioned action across five 3D tasks under a unified agent-environment loop. Given PNG images or sampled video frames and, where applicable, supplied 3D assets, an agent acts on a 3D environment. We evaluate each final output against hidden ground truth with task-specific geometric metrics. SceneActBench comprises five tasks built from 210 source instances, yielding 520 task cases including paired input conditions. Every task runs through one fixed agent loop to keep the comparison fair. Across eleven proprietary VLM configurations, Overall scores span 38.6-50.2, and none performs consistently well across tasks. We further analyse where and how failures manifest.